Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	53.539513
daal4py_KNeighborsClassifier	0.0	3.0	6.734521
KNeighborsClassifier_kd_tree	0.0	7.0	54.972622
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	52.993847
KMeans_tall	0.0	1.0	39.278736
daal4py_KMeans_tall	0.0	1.0	17.371433
KMeans_short	0.0	0.0	19.108480
daal4py_KMeans_short	0.0	0.0	7.865974
LogisticRegression	0.0	1.0	1.530703
daal4py_LogisticRegression	0.0	0.0	55.618301
Ridge	0.0	0.0	46.014546
daal4py_Ridge	0.0	0.0	14.818841
total	0.0	33.0	9.920891

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.142	0.003	1000000	1000000	100	brute	-1	1	NaN	NaN	0.487	0.004	0.292	0.006	See
1	KNeighborsClassifier	predict	0.161	0.010	1000000	1	100	brute	-1	1	1.0	1.0	0.096	0.003	1.680	0.120	See
2	KNeighborsClassifier	predict	28.035	0.378	1000000	1000	100	brute	-1	1	1.0	1.0	1.775	0.024	15.795	0.303	See
3	KNeighborsClassifier	fit	0.136	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.501	0.006	0.272	0.008	See
4	KNeighborsClassifier	predict	0.172	0.010	1000000	1	100	brute	-1	5	1.0	1.0	0.092	0.001	1.863	0.106	See
5	KNeighborsClassifier	predict	36.185	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	1.798	0.028	20.128	0.311	See
6	KNeighborsClassifier	fit	0.132	0.003	1000000	1000000	100	brute	-1	100	NaN	NaN	0.491	0.003	0.270	0.006	See
7	KNeighborsClassifier	predict	0.177	0.011	1000000	1	100	brute	-1	100	1.0	1.0	0.096	0.001	1.844	0.116	See
8	KNeighborsClassifier	predict	36.040	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	1.824	0.024	19.763	0.264	See
9	KNeighborsClassifier	fit	0.133	0.003	1000000	1000000	100	brute	1	1	NaN	NaN	0.500	0.008	0.266	0.007	See
10	KNeighborsClassifier	predict	0.181	0.002	1000000	1	100	brute	1	1	1.0	1.0	0.095	0.002	1.916	0.047	See
11	KNeighborsClassifier	predict	13.766	0.058	1000000	1000	100	brute	1	1	1.0	1.0	1.803	0.005	7.635	0.039	See
12	KNeighborsClassifier	fit	0.134	0.003	1000000	1000000	100	brute	1	5	NaN	NaN	0.490	0.004	0.274	0.007	See
13	KNeighborsClassifier	predict	0.193	0.001	1000000	1	100	brute	1	5	1.0	1.0	0.096	0.002	2.001	0.041	See
14	KNeighborsClassifier	predict	22.715	0.132	1000000	1000	100	brute	1	5	1.0	1.0	1.775	0.016	12.799	0.136	See
15	KNeighborsClassifier	fit	0.135	0.003	1000000	1000000	100	brute	1	100	NaN	NaN	0.495	0.008	0.272	0.007	See
16	KNeighborsClassifier	predict	0.193	0.001	1000000	1	100	brute	1	100	1.0	1.0	0.092	0.001	2.098	0.030	See
17	KNeighborsClassifier	predict	22.245	0.145	1000000	1000	100	brute	1	100	1.0	1.0	1.861	0.024	11.956	0.173	See
18	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	brute	-1	1	NaN	NaN	0.098	0.003	0.582	0.022	See
19	KNeighborsClassifier	predict	0.020	0.001	1000000	1	2	brute	-1	1	1.0	1.0	0.006	0.000	3.449	0.264	See
20	KNeighborsClassifier	predict	24.909	0.077	1000000	1000	2	brute	-1	1	1.0	1.0	0.261	0.005	95.353	1.685	See
21	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	-1	5	NaN	NaN	0.095	0.003	0.622	0.022	See
22	KNeighborsClassifier	predict	0.029	0.002	1000000	1	2	brute	-1	5	1.0	1.0	0.008	0.003	3.432	1.096	See
23	KNeighborsClassifier	predict	34.802	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.269	0.007	129.577	3.360	See
24	KNeighborsClassifier	fit	0.063	0.001	1000000	1000000	2	brute	-1	100	NaN	NaN	0.097	0.001	0.655	0.012	See
25	KNeighborsClassifier	predict	0.031	0.002	1000000	1	2	brute	-1	100	1.0	1.0	0.006	0.000	5.034	0.415	See
26	KNeighborsClassifier	predict	33.190	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.319	0.008	104.096	2.731	See
27	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	brute	1	1	NaN	NaN	0.100	0.003	0.567	0.020	See
28	KNeighborsClassifier	predict	0.017	0.000	1000000	1	2	brute	1	1	1.0	1.0	0.006	0.000	2.883	0.144	See
29	KNeighborsClassifier	predict	10.811	0.055	1000000	1000	2	brute	1	1	1.0	1.0	0.263	0.007	41.117	1.070	See
30	KNeighborsClassifier	fit	0.055	0.001	1000000	1000000	2	brute	1	5	NaN	NaN	0.097	0.001	0.572	0.010	See
31	KNeighborsClassifier	predict	0.024	0.001	1000000	1	2	brute	1	5	1.0	1.0	0.006	0.000	4.081	0.171	See
32	KNeighborsClassifier	predict	19.622	0.031	1000000	1000	2	brute	1	5	1.0	1.0	0.265	0.004	74.143	1.196	See
33	KNeighborsClassifier	fit	0.068	0.002	1000000	1000000	2	brute	1	100	NaN	NaN	0.098	0.001	0.692	0.022	See
34	KNeighborsClassifier	predict	0.025	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.006	0.000	4.126	0.229	See
35	KNeighborsClassifier	predict	19.572	0.086	1000000	1000	2	brute	1	100	1.0	1.0	0.322	0.005	60.864	0.955	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.429	0.066	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.801	0.015	4.278	0.116	See
1	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	20.179	17.881	See
2	KNeighborsClassifier_kd_tree	predict	0.498	0.007	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.130	0.002	3.834	0.069	See
3	KNeighborsClassifier_kd_tree	fit	3.600	0.035	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.828	0.008	4.349	0.059	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.000	0.000	13.059	7.443	See
5	KNeighborsClassifier_kd_tree	predict	0.966	0.005	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.232	0.003	4.158	0.061	See
6	KNeighborsClassifier_kd_tree	fit	3.729	0.040	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.801	0.011	4.655	0.080	See
7	KNeighborsClassifier_kd_tree	predict	0.005	0.000	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.000	0.000	9.316	4.779	See
8	KNeighborsClassifier_kd_tree	predict	3.178	0.028	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.708	0.007	4.490	0.059	See
9	KNeighborsClassifier_kd_tree	fit	3.533	0.077	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.827	0.014	4.272	0.117	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	5.411	4.407	See
11	KNeighborsClassifier_kd_tree	predict	0.860	0.005	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.130	0.003	6.609	0.151	See
12	KNeighborsClassifier_kd_tree	fit	3.589	0.048	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.801	0.010	4.481	0.082	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.000	0.000	4.849	3.605	See
14	KNeighborsClassifier_kd_tree	predict	1.615	0.011	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.234	0.002	6.892	0.071	See
15	KNeighborsClassifier_kd_tree	fit	3.627	0.051	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.838	0.008	4.330	0.073	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	1	100	1.0	1.0	0.000	0.000	5.572	3.093	See
17	KNeighborsClassifier_kd_tree	predict	5.421	0.015	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.694	0.003	7.809	0.041	See
18	KNeighborsClassifier_kd_tree	fit	2.122	0.094	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.500	0.007	4.247	0.197	See
19	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	19.122	15.959	See
20	KNeighborsClassifier_kd_tree	predict	0.024	0.001	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	31.255	12.925	See
21	KNeighborsClassifier_kd_tree	fit	2.114	0.049	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.514	0.010	4.117	0.123	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	21.497	18.696	See
23	KNeighborsClassifier_kd_tree	predict	0.027	0.001	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	23.178	7.191	See
24	KNeighborsClassifier_kd_tree	fit	2.149	0.087	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.506	0.021	4.248	0.244	See
25	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	14.576	11.833	See
26	KNeighborsClassifier_kd_tree	predict	0.049	0.004	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.007	0.001	6.532	0.730	See
27	KNeighborsClassifier_kd_tree	fit	2.030	0.090	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.514	0.017	3.950	0.219	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.703	5.606	See
29	KNeighborsClassifier_kd_tree	predict	0.022	0.000	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	31.017	13.898	See
30	KNeighborsClassifier_kd_tree	fit	1.963	0.151	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.490	0.016	4.004	0.336	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	5.127	4.979	See
32	KNeighborsClassifier_kd_tree	predict	0.024	0.000	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.002	0.002	12.868	15.166	See
33	KNeighborsClassifier_kd_tree	fit	1.937	0.100	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.510	0.014	3.801	0.221	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	4.466	3.855	See
35	KNeighborsClassifier_kd_tree	predict	0.055	0.001	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.010	0.006	5.334	3.031	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.600	0.050	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.392	0.015	1.531	0.140	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.768	1.603	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.623	2.346	See
3	KMeans_tall	fit	0.503	0.006	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.358	0.015	1.403	0.061	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.924	1.998	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.818	1.662	See
6	KMeans_tall	fit	6.375	0.105	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	3.147	0.048	2.026	0.045	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.604	1.315	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.052	1.365	See
9	KMeans_tall	fit	5.746	0.070	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	2.988	0.047	1.923	0.038	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.752	1.728	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.023	1.317	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.282	0.011	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	0.090	0.003	3.121	0.160	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.865	1.539	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.136	0.345	See
3	KMeans_short	fit	0.110	0.002	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.042	0.002	2.629	0.107	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.785	1.682	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.081	0.318	See
6	KMeans_short	fit	0.904	0.041	10000	10000	100	full	k-means++	30	300	1	0.0	19.0	NaN	20.0	NaN	0.335	0.011	2.703	0.150	See
7	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.610	1.016	See
8	KMeans_short	predict	0.007	0.003	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	6.232	2.530	See
9	KMeans_short	fit	0.261	0.033	10000	10000	100	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.184	0.019	1.416	0.233	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.680	1.161	See
11	KMeans_short	predict	0.008	0.001	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	6.387	1.309	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.151	0.026	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	11.414	0.034	0.977	0.004	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.379	0.446	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.788	0.496	See
3	LogisticRegression	fit	0.794	0.017	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[27]	0.793	0.025	1.002	0.038	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.002	0.002	0.059	0.085	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.003	0.000	0.546	0.138	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	1.723	0.045	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.916	0.032	1.882	0.083	See
1	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.668	0.827	See
2	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.001	0.000	0.810	0.509	See
3	Ridge	fit	1.134	0.016	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.229	0.003	4.945	0.094	See
4	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.706	0.879	See
5	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.535	0.415	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}